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5 Conclusions
In our study, we proposed a new artificial immune network model, Endocrine-
Immune Network (EINET) for optimization. Inspired by high-level regulation
principle of endocrine system, we utilized two mechanisms of endocrine, hor-
monal regulation and enzymatic reaction mechanisms, into the evolution pro-
cess of AIN model for EINET. In order to evaluate the performance of EINET,
a version of EINET, EINET-TSP algorithm, is designed and implemented suc-
cessfully to solving Traveling Salesman Problem. Experiments based on TSP
benchmark instances from TSPLIB shows that EINET has a promising perfor-
mance for combinatorial optimization problem. We also compare our algorithm
with the nine recent algorithms such as copt-aiNet, cob-aiNet[C], neuro-immune
network, etc. The computational results indicate that EINET-TSP was able to
obtain the global optimum for most of the problems studied, while simultane-
ously providing a set of high quality and diverse solutions. As future steps, our
future research will be mainly focused on the application of EINET in other
optimization problems. In particular, a parallel version of proposed model will
be an important objective in future work, which allows us to apply EINET to
more real problems.
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